Research on natural gas load forecasting based on least squares support vector machine

Abstract

Natural gas load forecasting is a key process to the efficient operation of pipeline network. An accurate forecast is required to guarantee a balanced network operation and ensure safe gas supply at a minimal cost. Machine learning techniques are finding more and more applications in the field of load forecasting. A novel regression technique, called support vector machines (SVM), based on the statistical learning theory is explored in this paper for the prediction of natural gas demands. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization supported by the conventional regression techniques. Least squares support vector machines (LS-SVM) is a kind of SVM that has different cost function with respect to standard SVM. The research result shows that the prediction accuracy of SVM is better than that of neural network. The software package NGPSLF based on LS-SVM prediction has been gone into practical business application.